Load Data
library(gdata)
dataset <- read.delim("raw_data/Figure2C.txt", stringsAsFactors = FALSE)
dataset$genotype <- factor(gsub(" ","",dataset$genotype))
dataset$experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each= length(levels(dataset$genotype))))
dataset$UID <- factor(paste(dataset$experiment, dataset$genotype))
kable(dataset, row.names = F)
| WT |
22.01058 |
14.99513 |
27.16387 |
40.27288 |
38.033250 |
exp1 |
exp1 WT |
| PARP1KO |
24.99867 |
28.56546 |
32.87948 |
28.45025 |
28.545320 |
exp1 |
exp1 PARP1KO |
| ALC1KO |
12.11620 |
39.52356 |
45.58298 |
61.03559 |
64.132780 |
exp1 |
exp1 ALC1KO |
| ALC1KOPARP1KO |
12.52204 |
14.80034 |
19.89682 |
26.65366 |
20.054400 |
exp1 |
exp1 ALC1KOPARP1KO |
| WT |
27.32625 |
31.84061 |
25.66672 |
29.00732 |
49.918700 |
exp2 |
exp2 WT |
| PARP1KO |
98.22148 |
135.57802 |
114.79064 |
85.43817 |
67.054973 |
exp2 |
exp2 PARP1KO |
| ALC1KO |
30.10425 |
120.77006 |
156.36509 |
152.38575 |
122.686347 |
exp2 |
exp2 ALC1KO |
| ALC1KOPARP1KO |
38.10620 |
20.10778 |
23.18652 |
24.74054 |
6.578637 |
exp2 |
exp2 ALC1KOPARP1KO |
| WT |
42.88251 |
45.64701 |
47.50491 |
52.09927 |
84.335243 |
exp3 |
exp3 WT |
| PARP1KO |
51.33273 |
91.06651 |
36.30963 |
30.68799 |
83.422833 |
exp3 |
exp3 PARP1KO |
| ALC1KO |
54.28341 |
141.70070 |
192.99382 |
189.15404 |
156.706603 |
exp3 |
exp3 ALC1KO |
| ALC1KOPARP1KO |
27.83733 |
50.46166 |
32.71616 |
28.70225 |
33.340863 |
exp3 |
exp3 ALC1KOPARP1KO |
library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "treatment", value.name = "Outcome")
dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT")
dataset$olaparib <- gsub("NT","1",dataset$treatment)
dataset$olaparib <- gsub("X|nM","",dataset$olaparib)
dataset$olaparib <- log10(as.integer(dataset$olaparib))
dataset$Offset <- NA
for(uid in levels(dataset$UID)){
dataset$Offset[dataset$UID == uid] <- mean(dataset$Outcome[dataset$UID == uid])
}
dataset$NormOutcome <- dataset$Outcome / dataset$Offset
dataset$Offset2 <- NA
for(gid in levels(dataset$genotype)){
dataset$Offset2[dataset$genotype == gid] <- mean(dataset$NormOutcome[dataset$genotype == gid & dataset$olaparib == 0])
}
dataset$NormOutcome2 <- dataset$NormOutcome / dataset$Offset2
kable(dataset, row.names = F)
| WT |
exp1 |
exp1 WT |
NT |
22.010580 |
0.000000 |
28.49514 |
0.7724327 |
0.7978993 |
0.9680830 |
| PARP1KO |
exp1 |
exp1 PARP1KO |
NT |
24.998670 |
0.000000 |
28.68784 |
0.8714031 |
0.9093397 |
0.9582812 |
| ALC1KO |
exp1 |
exp1 ALC1KO |
NT |
12.116200 |
0.000000 |
44.47822 |
0.2724075 |
0.3000842 |
0.9077700 |
| ALC1KOPARP1KO |
exp1 |
exp1 ALC1KOPARP1KO |
NT |
12.522040 |
0.000000 |
18.78545 |
0.6665818 |
1.0537221 |
0.6325973 |
| WT |
exp2 |
exp2 WT |
NT |
27.326250 |
0.000000 |
32.75192 |
0.8343404 |
0.7978993 |
1.0456713 |
| PARP1KO |
exp2 |
exp2 PARP1KO |
NT |
98.221477 |
0.000000 |
100.21666 |
0.9800913 |
0.9093397 |
1.0778056 |
| ALC1KO |
exp2 |
exp2 ALC1KO |
NT |
30.104250 |
0.000000 |
116.46230 |
0.2584892 |
0.3000842 |
0.8613889 |
| ALC1KOPARP1KO |
exp2 |
exp2 ALC1KOPARP1KO |
NT |
38.106200 |
0.000000 |
22.54393 |
1.6903084 |
1.0537221 |
1.6041311 |
| WT |
exp3 |
exp3 WT |
NT |
42.882513 |
0.000000 |
54.49379 |
0.7869248 |
0.7978993 |
0.9862457 |
| PARP1KO |
exp3 |
exp3 PARP1KO |
NT |
51.332727 |
0.000000 |
58.56394 |
0.8765245 |
0.9093397 |
0.9639132 |
| ALC1KO |
exp3 |
exp3 ALC1KO |
NT |
54.283413 |
0.000000 |
146.96772 |
0.3693560 |
0.3000842 |
1.2308412 |
| ALC1KOPARP1KO |
exp3 |
exp3 ALC1KOPARP1KO |
NT |
27.837327 |
0.000000 |
34.61165 |
0.8042761 |
1.0537221 |
0.7632716 |
| WT |
exp1 |
exp1 WT |
X30.nM |
14.995130 |
1.477121 |
28.49514 |
0.5262346 |
0.7978993 |
0.6595251 |
| PARP1KO |
exp1 |
exp1 PARP1KO |
X30.nM |
28.565460 |
1.477121 |
28.68784 |
0.9957342 |
0.9093397 |
1.0950080 |
| ALC1KO |
exp1 |
exp1 ALC1KO |
X30.nM |
39.523560 |
1.477121 |
44.47822 |
0.8886048 |
0.3000842 |
2.9611843 |
| ALC1KOPARP1KO |
exp1 |
exp1 ALC1KOPARP1KO |
X30.nM |
14.800340 |
1.477121 |
18.78545 |
0.7878618 |
1.0537221 |
0.7476941 |
| WT |
exp2 |
exp2 WT |
X30.nM |
31.840607 |
1.477121 |
32.75192 |
0.9721753 |
0.7978993 |
1.2184185 |
| PARP1KO |
exp2 |
exp2 PARP1KO |
X30.nM |
135.578023 |
1.477121 |
100.21666 |
1.3528492 |
0.9093397 |
1.4877270 |
| ALC1KO |
exp2 |
exp2 ALC1KO |
X30.nM |
120.770057 |
1.477121 |
116.46230 |
1.0369884 |
0.3000842 |
3.4556577 |
| ALC1KOPARP1KO |
exp2 |
exp2 ALC1KOPARP1KO |
X30.nM |
20.107777 |
1.477121 |
22.54393 |
0.8919373 |
1.0537221 |
0.8464636 |
| WT |
exp3 |
exp3 WT |
X30.nM |
45.647010 |
1.477121 |
54.49379 |
0.8376553 |
0.7978993 |
1.0498258 |
| PARP1KO |
exp3 |
exp3 PARP1KO |
X30.nM |
91.066513 |
1.477121 |
58.56394 |
1.5549930 |
0.9093397 |
1.7100244 |
| ALC1KO |
exp3 |
exp3 ALC1KO |
X30.nM |
141.700703 |
1.477121 |
146.96772 |
0.9641621 |
0.3000842 |
3.2129714 |
| ALC1KOPARP1KO |
exp3 |
exp3 ALC1KOPARP1KO |
X30.nM |
50.461660 |
1.477121 |
34.61165 |
1.4579385 |
1.0537221 |
1.3836081 |
| WT |
exp1 |
exp1 WT |
X300.nM |
27.163870 |
2.477121 |
28.49514 |
0.9532807 |
0.7978993 |
1.1947382 |
| PARP1KO |
exp1 |
exp1 PARP1KO |
X300.nM |
32.879480 |
2.477121 |
28.68784 |
1.1461122 |
0.9093397 |
1.2603786 |
| ALC1KO |
exp1 |
exp1 ALC1KO |
X300.nM |
45.582980 |
2.477121 |
44.47822 |
1.0248382 |
0.3000842 |
3.4151682 |
| ALC1KOPARP1KO |
exp1 |
exp1 ALC1KOPARP1KO |
X300.nM |
19.896820 |
2.477121 |
18.78545 |
1.0591611 |
1.0537221 |
1.0051617 |
| WT |
exp2 |
exp2 WT |
X300.nM |
25.666717 |
2.477121 |
32.75192 |
0.7836706 |
0.7978993 |
0.9821673 |
| PARP1KO |
exp2 |
exp2 PARP1KO |
X300.nM |
114.790640 |
2.477121 |
100.21666 |
1.1454248 |
0.9093397 |
1.2596226 |
| ALC1KO |
exp2 |
exp2 ALC1KO |
X300.nM |
156.365093 |
2.477121 |
116.46230 |
1.3426241 |
0.3000842 |
4.4741573 |
| ALC1KOPARP1KO |
exp2 |
exp2 ALC1KOPARP1KO |
X300.nM |
23.186517 |
2.477121 |
22.54393 |
1.0285036 |
1.0537221 |
0.9760672 |
| WT |
exp3 |
exp3 WT |
X300.nM |
47.504913 |
2.477121 |
54.49379 |
0.8717491 |
0.7978993 |
1.0925553 |
| PARP1KO |
exp3 |
exp3 PARP1KO |
X300.nM |
36.309627 |
2.477121 |
58.56394 |
0.6199998 |
0.9093397 |
0.6818132 |
| ALC1KO |
exp3 |
exp3 ALC1KO |
X300.nM |
192.993817 |
2.477121 |
146.96772 |
1.3131715 |
0.3000842 |
4.3760095 |
| ALC1KOPARP1KO |
exp3 |
exp3 ALC1KOPARP1KO |
X300.nM |
32.716163 |
2.477121 |
34.61165 |
0.9452355 |
1.0537221 |
0.8970444 |
| WT |
exp1 |
exp1 WT |
X3000.nM |
40.272880 |
3.477121 |
28.49514 |
1.4133244 |
0.7978993 |
1.7713067 |
| PARP1KO |
exp1 |
exp1 PARP1KO |
X3000.nM |
28.450250 |
3.477121 |
28.68784 |
0.9917182 |
0.9093397 |
1.0905916 |
| ALC1KO |
exp1 |
exp1 ALC1KO |
X3000.nM |
61.035590 |
3.477121 |
44.47822 |
1.3722579 |
0.3000842 |
4.5729087 |
| ALC1KOPARP1KO |
exp1 |
exp1 ALC1KOPARP1KO |
X3000.nM |
26.653660 |
3.477121 |
18.78545 |
1.4188458 |
1.0537221 |
1.3465086 |
| WT |
exp2 |
exp2 WT |
X3000.nM |
29.007323 |
3.477121 |
32.75192 |
0.8856679 |
0.7978993 |
1.1099996 |
| PARP1KO |
exp2 |
exp2 PARP1KO |
X3000.nM |
85.438167 |
3.477121 |
100.21666 |
0.8525346 |
0.9093397 |
0.9375315 |
| ALC1KO |
exp2 |
exp2 ALC1KO |
X3000.nM |
152.385750 |
3.477121 |
116.46230 |
1.3084556 |
0.3000842 |
4.3602943 |
| ALC1KOPARP1KO |
exp2 |
exp2 ALC1KOPARP1KO |
X3000.nM |
24.740540 |
3.477121 |
22.54393 |
1.0974367 |
1.0537221 |
1.0414859 |
| WT |
exp3 |
exp3 WT |
X3000.nM |
52.099270 |
3.477121 |
54.49379 |
0.9560588 |
0.7978993 |
1.1982199 |
| PARP1KO |
exp3 |
exp3 PARP1KO |
X3000.nM |
30.687987 |
3.477121 |
58.56394 |
0.5240083 |
0.9093397 |
0.5762514 |
| ALC1KO |
exp3 |
exp3 ALC1KO |
X3000.nM |
189.154040 |
3.477121 |
146.96772 |
1.2870448 |
0.3000842 |
4.2889450 |
| ALC1KOPARP1KO |
exp3 |
exp3 ALC1KOPARP1KO |
X3000.nM |
28.702250 |
3.477121 |
34.61165 |
0.8292655 |
1.0537221 |
0.7869869 |
| WT |
exp1 |
exp1 WT |
X30000.nM |
38.033250 |
4.477121 |
28.49514 |
1.3347275 |
0.7978993 |
1.6728020 |
| PARP1KO |
exp1 |
exp1 PARP1KO |
X30000.nM |
28.545320 |
4.477121 |
28.68784 |
0.9950322 |
0.9093397 |
1.0942360 |
| ALC1KO |
exp1 |
exp1 ALC1KO |
X30000.nM |
64.132780 |
4.477121 |
44.47822 |
1.4418917 |
0.3000842 |
4.8049564 |
| ALC1KOPARP1KO |
exp1 |
exp1 ALC1KOPARP1KO |
X30000.nM |
20.054400 |
4.477121 |
18.78545 |
1.0675495 |
1.0537221 |
1.0131224 |
| WT |
exp2 |
exp2 WT |
X30000.nM |
49.918700 |
4.477121 |
32.75192 |
1.5241458 |
0.7978993 |
1.9101982 |
| PARP1KO |
exp2 |
exp2 PARP1KO |
X30000.nM |
67.054973 |
4.477121 |
100.21666 |
0.6691001 |
0.9093397 |
0.7358088 |
| ALC1KO |
exp2 |
exp2 ALC1KO |
X30000.nM |
122.686347 |
4.477121 |
116.46230 |
1.0534426 |
0.3000842 |
3.5104895 |
| ALC1KOPARP1KO |
exp2 |
exp2 ALC1KOPARP1KO |
X30000.nM |
6.578637 |
4.477121 |
22.54393 |
0.2918140 |
1.0537221 |
0.2769364 |
| WT |
exp3 |
exp3 WT |
X30000.nM |
84.335243 |
4.477121 |
54.49379 |
1.5476120 |
0.7978993 |
1.9396082 |
| PARP1KO |
exp3 |
exp3 PARP1KO |
X30000.nM |
83.422833 |
4.477121 |
58.56394 |
1.4244745 |
0.9093397 |
1.5664933 |
| ALC1KO |
exp3 |
exp3 ALC1KO |
X30000.nM |
156.706603 |
4.477121 |
146.96772 |
1.0662655 |
0.3000842 |
3.5532205 |
| ALC1KOPARP1KO |
exp3 |
exp3 ALC1KOPARP1KO |
X30000.nM |
33.340863 |
4.477121 |
34.61165 |
0.9632843 |
1.0537221 |
0.9141731 |
Plot Data
library(ggplot2)
# raw data
ggplot(dataset, aes(x=olaparib, y=Outcome)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=genotype)) +
geom_point(aes(colour=genotype, shape=experiment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_shape_manual(values=15:20) +
scale_color_manual(values=c("#000000","#000080","#808080","#800000"))

# norm
ggplot(dataset, aes(x=olaparib, y=NormOutcome)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=genotype)) +
geom_point(aes(colour=genotype, shape=experiment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_shape_manual(values=15:20) +
scale_color_manual(values=c("#000000","#000080","#808080","#800000"))

# norm2
ggplot(dataset, aes(x=olaparib, y=NormOutcome2)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=genotype)) +
geom_point(aes(colour=genotype, shape=experiment), size=2) +
facet_grid(. ~ genotype) +
xlab(label = "Olaparib (log10 nM)") +
scale_shape_manual(values=15:20) +
scale_color_manual(values=c("#000000","#000080","#808080","#800000"))

dataplot <- aggregate(NormOutcome2 ~ genotype+treatment, data = dataset, mean)
dataplot$se <- aggregate(NormOutcome2 ~ genotype+treatment, data = dataset, FUN = function(x){ sd(x)/sqrt(length(x))})$NormOutcome2
dataplot$treatment <- factor(gsub("X|.nM","", dataplot$treatment))
dataplot$genotype <- factor(dataplot$genotype, levels = c("WT","PARP1KO","ALC1KO","ALC1KOPARP1KO"))
dataplot$treatment <- relevel(dataplot$treatment, ref = "NT")
ggplot(dataplot, aes(x=treatment, y=NormOutcome2, fill = genotype)) +
theme_bw() +
theme(panel.grid=element_blank(), text = element_text(size=14)) +
geom_bar(stat="identity", position=position_dodge()) +
geom_errorbar(aes(ymin=NormOutcome2-se, ymax=NormOutcome2+se),width=.2, position=position_dodge(.9)) +
ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
xlab(label = "Olaparib (nM)") +
ylim(0, 6) +
scale_fill_manual(values=c("#000000","#800000","#000080","#808080"))

library(Cairo)
cairo_pdf("Figure2C_v1.pdf", width = 8, height = 4, family = "Arial")
ggplot(dataplot, aes(x=treatment, y=NormOutcome2, fill = genotype)) +
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
axis.line = element_line(colour = "black"), text = element_text(size=14),
panel.border = element_blank(), panel.background = element_blank()) +
geom_bar(stat="identity", position=position_dodge()) +
geom_errorbar(aes(ymin=NormOutcome2-se, ymax=NormOutcome2+se),width=.2, position=position_dodge(.9)) +
ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
xlab(label = "Olaparib (nM)") +
ylim(0, 6) +
scale_fill_manual(values=c("#000000","#800000","#000080","#808080"))
dev.off()
## quartz_off_screen
## 2
dataset$genotype <- factor(dataset$genotype, levels = c("WT","PARP1KO","ALC1KO","ALC1KOPARP1KO"))
dataset$treatment <- relevel(dataset$treatment, ref = "NT")
dataset$treatment <- factor(gsub("X|.nM","", dataset$treatment), levels =c("NT","30","300","3000","30000") )
cairo_pdf("Figure2C_v2.pdf", width = 8, height = 4, family = "Arial")
ggplot(dataplot, aes(x=treatment, y=NormOutcome2, fill = genotype)) +
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
axis.line = element_line(colour = "black"), text = element_text(size=14),
panel.border = element_blank(), panel.background = element_blank()) +
geom_bar(stat="identity", position=position_dodge()) +
geom_bar(stat="identity", position=position_dodge(), colour="black") +
ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
xlab(label = "Olaparib (nM)") +
ylim(0, 6) +
scale_fill_manual(values=c("#00000080","#80000080","#00008080","#80808080")) +
geom_jitter(data = dataset, cex=1, position=position_dodge(0.9), aes(colour=genotype)) +
scale_color_manual(values=c("#000000","#800000","#000080","#808080")) +
geom_errorbar(aes(ymin=NormOutcome2-se, ymax=NormOutcome2+se),width=.2, position=position_dodge(.9))
dev.off()
## quartz_off_screen
## 2
cairo_pdf("Figure2C_v3.pdf", width = 8, height = 4, family = "Arial")
ggplot(dataset, aes(x=treatment, y=NormOutcome2, color=genotype)) +
theme_bw() +
theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(),
axis.line = element_line(colour = "black"), text = element_text(size=14),
panel.border = element_blank(), panel.background = element_blank()) +
#geom_bar(stat="identity", position=position_dodge()) +
geom_jitter(cex=1, position=position_dodge(1)) +
stat_summary(fun.data=mean_se, fun.args = list(mult=1), geom="errorbar", width=0.25, aes(colour=genotype), position=position_dodge(1)) +
stat_summary(fun.y=mean, geom="crossbar", width=0.5, aes(colour=genotype), position=position_dodge(1)) +
ylab(label = paste0("Relative ",expression("\u03B3"),"H2AX intensity")) +
xlab(label = "Olaparib (nM)") +
ylim(0, 6) +
scale_fill_manual(values=c("#000000","#800000","#000080","#808080")) +
scale_color_manual(values=c("#000000","#800000","#000080","#808080"))
dev.off()
## quartz_off_screen
## 2
Linear Model
library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)
fit1 <- lm(NormOutcome2 ~ olaparib*genotype, data = dataset)
print(summary(fit1))
##
## Call:
## lm(formula = NormOutcome2 ~ olaparib * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.24699 -0.24173 0.03446 0.25248 1.07686
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8226 0.2236 3.679 0.000556 ***
## olaparib 0.1808 0.0786 2.301 0.025445 *
## genotypePARP1KO 0.3265 0.3162 1.033 0.306487
## genotypeALC1KO 0.8901 0.3162 2.815 0.006873 **
## genotypeALC1KOPARP1KO 0.2228 0.3162 0.705 0.484186
## olaparib:genotypePARP1KO -0.2016 0.1112 -1.814 0.075516 .
## olaparib:genotypeALC1KO 0.4993 0.1112 4.491 3.97e-05 ***
## olaparib:genotypeALC1KOPARP1KO -0.2213 0.1112 -1.991 0.051766 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4734 on 52 degrees of freedom
## Multiple R-squared: 0.8652, Adjusted R-squared: 0.847
## F-statistic: 47.66 on 7 and 52 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit1))
## AIC: 89.94364
simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormOutcome2 ~ poly(olaparib,2)*genotype, data = dataset)
print(summary(fit2))
##
## Call:
## lm(formula = NormOutcome2 ~ poly(olaparib, 2) * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67569 -0.16602 -0.01959 0.13902 0.81110
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.25329 0.08562 14.638 < 2e-16
## poly(olaparib, 2)1 2.17830 0.66321 3.284 0.00191
## poly(olaparib, 2)2 1.23615 0.66321 1.864 0.06846
## genotypePARP1KO -0.15359 0.12109 -1.268 0.21075
## genotypeALC1KO 2.07911 0.12109 17.171 < 2e-16
## genotypeALC1KOPARP1KO -0.30427 0.12109 -2.513 0.01539
## poly(olaparib, 2)1:genotypePARP1KO -2.42823 0.93792 -2.589 0.01270
## poly(olaparib, 2)2:genotypePARP1KO -1.60138 0.93792 -1.707 0.09422
## poly(olaparib, 2)1:genotypeALC1KO 6.01356 0.93792 6.412 5.88e-08
## poly(olaparib, 2)2:genotypeALC1KO -6.09689 0.93792 -6.500 4.29e-08
## poly(olaparib, 2)1:genotypeALC1KOPARP1KO -2.66556 0.93792 -2.842 0.00656
## poly(olaparib, 2)2:genotypeALC1KOPARP1KO -1.69364 0.93792 -1.806 0.07723
##
## (Intercept) ***
## poly(olaparib, 2)1 **
## poly(olaparib, 2)2 .
## genotypePARP1KO
## genotypeALC1KO ***
## genotypeALC1KOPARP1KO *
## poly(olaparib, 2)1:genotypePARP1KO *
## poly(olaparib, 2)2:genotypePARP1KO .
## poly(olaparib, 2)1:genotypeALC1KO ***
## poly(olaparib, 2)2:genotypeALC1KO ***
## poly(olaparib, 2)1:genotypeALC1KOPARP1KO **
## poly(olaparib, 2)2:genotypeALC1KOPARP1KO .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3316 on 48 degrees of freedom
## Multiple R-squared: 0.9389, Adjusted R-squared: 0.9249
## F-statistic: 67.08 on 11 and 48 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit2))
## AIC: 50.42708
simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormOutcome2 ~ poly(olaparib,3)*genotype, data = dataset)
print(summary(fit3))
##
## Call:
## lm(formula = NormOutcome2 ~ poly(olaparib, 3) * genotype, data = dataset)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.68893 -0.17760 -0.01805 0.16931 0.84585
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.25329 0.08458 14.818 < 2e-16
## poly(olaparib, 3)1 2.17830 0.65516 3.325 0.00179
## poly(olaparib, 3)2 1.23615 0.65516 1.887 0.06580
## poly(olaparib, 3)3 0.14096 0.65516 0.215 0.83065
## genotypePARP1KO -0.15359 0.11962 -1.284 0.20585
## genotypeALC1KO 2.07911 0.11962 17.382 < 2e-16
## genotypeALC1KOPARP1KO -0.30427 0.11962 -2.544 0.01456
## poly(olaparib, 3)1:genotypePARP1KO -2.42823 0.92654 -2.621 0.01200
## poly(olaparib, 3)2:genotypePARP1KO -1.60138 0.92654 -1.728 0.09094
## poly(olaparib, 3)3:genotypePARP1KO 1.23345 0.92654 1.331 0.18996
## poly(olaparib, 3)1:genotypeALC1KO 6.01356 0.92654 6.490 6.41e-08
## poly(olaparib, 3)2:genotypeALC1KO -6.09689 0.92654 -6.580 4.73e-08
## poly(olaparib, 3)3:genotypeALC1KO -0.48503 0.92654 -0.523 0.60326
## poly(olaparib, 3)1:genotypeALC1KOPARP1KO -2.66556 0.92654 -2.877 0.00617
## poly(olaparib, 3)2:genotypeALC1KOPARP1KO -1.69364 0.92654 -1.828 0.07435
## poly(olaparib, 3)3:genotypeALC1KOPARP1KO -0.58714 0.92654 -0.634 0.52957
##
## (Intercept) ***
## poly(olaparib, 3)1 **
## poly(olaparib, 3)2 .
## poly(olaparib, 3)3
## genotypePARP1KO
## genotypeALC1KO ***
## genotypeALC1KOPARP1KO *
## poly(olaparib, 3)1:genotypePARP1KO *
## poly(olaparib, 3)2:genotypePARP1KO .
## poly(olaparib, 3)3:genotypePARP1KO
## poly(olaparib, 3)1:genotypeALC1KO ***
## poly(olaparib, 3)2:genotypeALC1KO ***
## poly(olaparib, 3)3:genotypeALC1KO
## poly(olaparib, 3)1:genotypeALC1KOPARP1KO **
## poly(olaparib, 3)2:genotypeALC1KOPARP1KO .
## poly(olaparib, 3)3:genotypeALC1KOPARP1KO
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3276 on 44 degrees of freedom
## Multiple R-squared: 0.9454, Adjusted R-squared: 0.9267
## F-statistic: 50.75 on 15 and 44 DF, p-value: < 2.2e-16
cat("AIC: ", AIC(fit3))
## AIC: 51.74104
simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

Compare Results
ICtab(fit1,fit2,
fit3,
base=T)
## AIC dAIC df
## fit2 50.4 0.0 13
## fit3 51.7 1.3 17
## fit1 89.9 39.5 9
Final Result
fit <- fit2
output <- coef(summary(fit))
output <- output[grep("olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
# suggested result table
kable(output, row.names = T)
| olaparib1 in WT |
2.178301 |
0.6632120 |
3.284471 |
0.0019121 |
| olaparib2 in WT |
1.236153 |
0.6632120 |
1.863888 |
0.0684602 |
| olaparib1: WT vs. PARP1KO |
-2.428227 |
0.9379234 |
-2.588939 |
0.0127045 |
| olaparib2: WT vs. PARP1KO |
-1.601379 |
0.9379234 |
-1.707367 |
0.0942155 |
| olaparib1: WT vs. ALC1KO |
6.013559 |
0.9379234 |
6.411567 |
0.0000001 |
| olaparib2: WT vs. ALC1KO |
-6.096891 |
0.9379234 |
-6.500414 |
0.0000000 |
| olaparib1: WT vs. ALC1KOPARP1KO |
-2.665563 |
0.9379234 |
-2.841984 |
0.0065634 |
| olaparib2: WT vs. ALC1KOPARP1KO |
-1.693642 |
0.9379234 |
-1.805735 |
0.0772327 |
write.table(output, file = "Figure2C_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1KO")
fit <- lm(NormOutcome2 ~ poly(olaparib,2)*genotype, data = dataset)
output <- coef(summary(fit))
output <- output[grep("olaparib", rownames(output)),]
rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype", paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1], sep = " in " )
# suggested result table
kable(output, row.names = T)
| olaparib1 in ALC1KO |
8.191859 |
0.6632120 |
12.351796 |
0.00e+00 |
| olaparib2 in ALC1KO |
-4.860738 |
0.6632120 |
-7.329087 |
0.00e+00 |
| olaparib1: ALC1KO vs. WT |
-6.013559 |
0.9379234 |
-6.411567 |
1.00e-07 |
| olaparib2: ALC1KO vs. WT |
6.096891 |
0.9379234 |
6.500414 |
0.00e+00 |
| olaparib1: ALC1KO vs. PARP1KO |
-8.441785 |
0.9379234 |
-9.000506 |
0.00e+00 |
| olaparib2: ALC1KO vs. PARP1KO |
4.495512 |
0.9379234 |
4.793048 |
1.63e-05 |
| olaparib1: ALC1KO vs. ALC1KOPARP1KO |
-8.679122 |
0.9379234 |
-9.253551 |
0.00e+00 |
| olaparib2: ALC1KO vs. ALC1KOPARP1KO |
4.403249 |
0.9379234 |
4.694679 |
2.26e-05 |
write.table(output, file = "Figure2C_Stats_Ref_ALC1KO.txt", quote = F, sep = "\t", row.names = T, col.names = NA)
ANOVA
fit2a <- lm(NormOutcome2 ~ poly(olaparib,2)*genotype, data = dataset)
fit2b <- lm(NormOutcome2 ~ poly(olaparib,2)+genotype, data = dataset)
anova(fit2a, fit2b)
## Analysis of Variance Table
##
## Model 1: NormOutcome2 ~ poly(olaparib, 2) * genotype
## Model 2: NormOutcome2 ~ poly(olaparib, 2) + genotype
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 48 5.2782
## 2 54 22.6547 -6 -17.377 26.337 1.282e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
New analysis
- fit model for each conc. independently
dataset$genotype <- relevel(dataset$genotype, ref = "WT")
for(i in 2:length(levels(dataset$treatment))){
dataset_sub <- dataset[dataset$treatment == levels(dataset$treatment)[i],]
for(g in seq_along(levels(dataset_sub$genotype))){
cat("Olaparib: ", levels(dataset$treatment)[i], "nM", "\n",
"Genotype: ", levels(dataset$genotype)[g], "\n")
dataset_sub$genotype <- relevel(dataset_sub$genotype, ref = levels(dataset_sub$genotype)[g])
fit_sub <- lm(NormOutcome2 ~ genotype, data = dataset_sub)
print(summary(fit_sub))
cat("AIC: ", AIC(fit_sub))
simres <- simulateResiduals(fittedModel = fit_sub)
plot(simres)
if(i == 2 & g == 1){
output <- coef(summary(fit_sub))
output <- output[grep("genotype", rownames(output)),]
rownames(output) <- paste0(gsub("genotype", paste0(" ",levels(dataset$genotype)[g], " vs. "), rownames(output)),
" in Olaparib ", levels(dataset$treatment)[i], "nM")
} else {
outtmp <- coef(summary(fit_sub))
outtmp <- outtmp[grep("genotype", rownames(outtmp)),]
rownames(outtmp) <- paste0(gsub("genotype", paste0(" ",levels(dataset$genotype)[g], " vs. "), rownames(outtmp)),
" in Olaparib ", levels(dataset$treatment)[i], "nM")
output <- rbind(output,outtmp)
if(g == length(levels(dataset_sub$genotype)) & i < length(levels(dataset$treatment))){
output <- rbind(output, " ", colnames(output))
}
}
}
}
## Olaparib: 30 nM
## Genotype: WT
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33591 -0.24586 0.02992 0.24330 0.39102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.97592 0.17258 5.655 0.000479 ***
## genotypeALC1KO 2.23401 0.24407 9.153 1.64e-05 ***
## genotypePARP1KO 0.45500 0.24407 1.864 0.099284 .
## genotypeALC1KOPARP1KO 0.01667 0.24407 0.068 0.947237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared: 0.9339, Adjusted R-squared: 0.9091
## F-statistic: 37.69 on 3 and 8 DF, p-value: 4.565e-05
##
## AIC: 10.20702

## Olaparib: 30 nM
## Genotype: ALC1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33591 -0.24586 0.02992 0.24330 0.39102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.2099 0.1726 18.600 7.20e-08 ***
## genotypeWT -2.2340 0.2441 -9.153 1.64e-05 ***
## genotypePARP1KO -1.7790 0.2441 -7.289 8.48e-05 ***
## genotypeALC1KOPARP1KO -2.2173 0.2441 -9.085 1.73e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared: 0.9339, Adjusted R-squared: 0.9091
## F-statistic: 37.69 on 3 and 8 DF, p-value: 4.565e-05
##
## AIC: 10.20702

## Olaparib: 30 nM
## Genotype: PARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33591 -0.24586 0.02992 0.24330 0.39102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.4309 0.1726 8.291 3.37e-05 ***
## genotypeALC1KO 1.7790 0.2441 7.289 8.48e-05 ***
## genotypeWT -0.4550 0.2441 -1.864 0.0993 .
## genotypeALC1KOPARP1KO -0.4383 0.2441 -1.796 0.1102
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared: 0.9339, Adjusted R-squared: 0.9091
## F-statistic: 37.69 on 3 and 8 DF, p-value: 4.565e-05
##
## AIC: 10.20702

## Olaparib: 30 nM
## Genotype: ALC1KOPARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33591 -0.24586 0.02992 0.24330 0.39102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.99259 0.17258 5.751 0.000428 ***
## genotypePARP1KO 0.43833 0.24407 1.796 0.110234
## genotypeALC1KO 2.21735 0.24407 9.085 1.73e-05 ***
## genotypeWT -0.01667 0.24407 -0.068 0.947237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2989 on 8 degrees of freedom
## Multiple R-squared: 0.9339, Adjusted R-squared: 0.9091
## F-statistic: 37.69 on 3 and 8 DF, p-value: 4.565e-05
##
## AIC: 10.20702

## Olaparib: 300 nM
## Genotype: WT
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67328 -0.07370 0.03119 0.19254 0.38571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.08982 0.19754 5.517 0.000562 ***
## genotypeALC1KO 2.99862 0.27936 10.734 4.99e-06 ***
## genotypePARP1KO -0.02255 0.27936 -0.081 0.937650
## genotypeALC1KOPARP1KO -0.13040 0.27936 -0.467 0.653115
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared: 0.9572, Adjusted R-squared: 0.9412
## F-statistic: 59.67 on 3 and 8 DF, p-value: 8.101e-06
##
## AIC: 13.44839

## Olaparib: 300 nM
## Genotype: ALC1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67328 -0.07370 0.03119 0.19254 0.38571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.0884 0.1975 20.70 3.11e-08 ***
## genotypeWT -2.9986 0.2794 -10.73 4.99e-06 ***
## genotypePARP1KO -3.0212 0.2794 -10.81 4.72e-06 ***
## genotypeALC1KOPARP1KO -3.1290 0.2794 -11.20 3.62e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared: 0.9572, Adjusted R-squared: 0.9412
## F-statistic: 59.67 on 3 and 8 DF, p-value: 8.101e-06
##
## AIC: 13.44839

## Olaparib: 300 nM
## Genotype: PARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67328 -0.07370 0.03119 0.19254 0.38571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.06727 0.19754 5.403 0.000644 ***
## genotypeALC1KO 3.02117 0.27936 10.815 4.72e-06 ***
## genotypeWT 0.02255 0.27936 0.081 0.937650
## genotypeALC1KOPARP1KO -0.10785 0.27936 -0.386 0.709523
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared: 0.9572, Adjusted R-squared: 0.9412
## F-statistic: 59.67 on 3 and 8 DF, p-value: 8.101e-06
##
## AIC: 13.44839

## Olaparib: 300 nM
## Genotype: ALC1KOPARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67328 -0.07370 0.03119 0.19254 0.38571
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.9594 0.1975 4.857 0.00126 **
## genotypePARP1KO 0.1078 0.2794 0.386 0.70952
## genotypeALC1KO 3.1290 0.2794 11.201 3.62e-06 ***
## genotypeWT 0.1304 0.2794 0.467 0.65311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3421 on 8 degrees of freedom
## Multiple R-squared: 0.9572, Adjusted R-squared: 0.9412
## F-statistic: 59.67 on 3 and 8 DF, p-value: 8.101e-06
##
## AIC: 13.44839

## Olaparib: 3000 nM
## Genotype: WT
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29187 -0.18368 -0.03196 0.17976 0.41146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.3598 0.1578 8.615 2.55e-05 ***
## genotypeALC1KO 3.0475 0.2232 13.652 7.98e-07 ***
## genotypePARP1KO -0.4917 0.2232 -2.203 0.0587 .
## genotypeALC1KOPARP1KO -0.3015 0.2232 -1.351 0.2137
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared: 0.9767, Adjusted R-squared: 0.9679
## F-statistic: 111.7 on 3 and 8 DF, p-value: 7.203e-07
##
## AIC: 8.064594

## Olaparib: 3000 nM
## Genotype: ALC1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29187 -0.18368 -0.03196 0.17976 0.41146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.4074 0.1578 27.92 2.92e-09 ***
## genotypeWT -3.0475 0.2232 -13.65 7.98e-07 ***
## genotypePARP1KO -3.5393 0.2232 -15.86 2.51e-07 ***
## genotypeALC1KOPARP1KO -3.3491 0.2232 -15.00 3.85e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared: 0.9767, Adjusted R-squared: 0.9679
## F-statistic: 111.7 on 3 and 8 DF, p-value: 7.203e-07
##
## AIC: 8.064594

## Olaparib: 3000 nM
## Genotype: PARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29187 -0.18368 -0.03196 0.17976 0.41146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.8681 0.1578 5.500 0.000574 ***
## genotypeALC1KO 3.5393 0.2232 15.855 2.51e-07 ***
## genotypeWT 0.4917 0.2232 2.203 0.058737 .
## genotypeALC1KOPARP1KO 0.1902 0.2232 0.852 0.418951
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared: 0.9767, Adjusted R-squared: 0.9679
## F-statistic: 111.7 on 3 and 8 DF, p-value: 7.203e-07
##
## AIC: 8.064594

## Olaparib: 3000 nM
## Genotype: ALC1KOPARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.29187 -0.18368 -0.03196 0.17976 0.41146
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0583 0.1578 6.705 0.000152 ***
## genotypePARP1KO -0.1902 0.2232 -0.852 0.418951
## genotypeALC1KO 3.3491 0.2232 15.003 3.85e-07 ***
## genotypeWT 0.3015 0.2232 1.351 0.213743
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2734 on 8 degrees of freedom
## Multiple R-squared: 0.9767, Adjusted R-squared: 0.9679
## F-statistic: 111.7 on 3 and 8 DF, p-value: 7.203e-07
##
## AIC: 8.064594

## Olaparib: 30000 nM
## Genotype: WT
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45781 -0.39803 0.01569 0.20417 0.84873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.8409 0.2732 6.739 0.000147 ***
## genotypeALC1KO 2.1154 0.3863 5.476 0.000590 ***
## genotypePARP1KO -0.7087 0.3863 -1.835 0.103906
## genotypeALC1KOPARP1KO -1.1061 0.3863 -2.863 0.021041 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared: 0.9119, Adjusted R-squared: 0.8789
## F-statistic: 27.6 on 3 and 8 DF, p-value: 0.000143
##
## AIC: 21.22689

## Olaparib: 30000 nM
## Genotype: ALC1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45781 -0.39803 0.01569 0.20417 0.84873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.9562 0.2732 14.484 5.05e-07 ***
## genotypeWT -2.1154 0.3863 -5.476 0.00059 ***
## genotypePARP1KO -2.8240 0.3863 -7.311 8.30e-05 ***
## genotypeALC1KOPARP1KO -3.2215 0.3863 -8.339 3.23e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared: 0.9119, Adjusted R-squared: 0.8789
## F-statistic: 27.6 on 3 and 8 DF, p-value: 0.000143
##
## AIC: 21.22689

## Olaparib: 30000 nM
## Genotype: PARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45781 -0.39803 0.01569 0.20417 0.84873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.1322 0.2732 4.145 0.00323 **
## genotypeALC1KO 2.8240 0.3863 7.311 8.3e-05 ***
## genotypeWT 0.7087 0.3863 1.835 0.10391
## genotypeALC1KOPARP1KO -0.3974 0.3863 -1.029 0.33366
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared: 0.9119, Adjusted R-squared: 0.8789
## F-statistic: 27.6 on 3 and 8 DF, p-value: 0.000143
##
## AIC: 21.22689

## Olaparib: 30000 nM
## Genotype: ALC1KOPARP1KO
##
## Call:
## lm(formula = NormOutcome2 ~ genotype, data = dataset_sub)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.45781 -0.39803 0.01569 0.20417 0.84873
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7347 0.2732 2.690 0.0275 *
## genotypePARP1KO 0.3974 0.3863 1.029 0.3337
## genotypeALC1KO 3.2215 0.3863 8.339 3.23e-05 ***
## genotypeWT 1.1061 0.3863 2.863 0.0210 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4731 on 8 degrees of freedom
## Multiple R-squared: 0.9119, Adjusted R-squared: 0.8789
## F-statistic: 27.6 on 3 and 8 DF, p-value: 0.000143
##
## AIC: 21.22689

# suggested result table
kable(output, row.names = T)
| WT vs. ALC1KO in Olaparib 30nM |
2.23401467145659 |
0.244066996853167 |
9.153284549982 |
1.63647502322034e-05 |
| WT vs. PARP1KO in Olaparib 30nM |
0.454996688324251 |
0.244066996853167 |
1.86422865111083 |
0.0992836079937305 |
| WT vs. ALC1KOPARP1KO in Olaparib 30nM |
0.0166654665999983 |
0.244066996853167 |
0.0682823438435818 |
0.947236691399539 |
| ALC1KO vs. WT in Olaparib 30nM |
-2.23401467145659 |
0.244066996853167 |
-9.153284549982 |
1.63647502322033e-05 |
| ALC1KO vs. PARP1KO in Olaparib 30nM |
-1.77901798313234 |
0.244066996853167 |
-7.28905589887117 |
8.47805458400702e-05 |
| ALC1KO vs. ALC1KOPARP1KO in Olaparib 30nM |
-2.2173492048566 |
0.244066996853167 |
-9.08500220613842 |
1.72928582043792e-05 |
| PARP1KO vs. ALC1KO in Olaparib 30nM |
1.77901798313234 |
0.244066996853167 |
7.28905589887116 |
8.47805458400707e-05 |
| PARP1KO vs. WT in Olaparib 30nM |
-0.454996688324251 |
0.244066996853167 |
-1.86422865111083 |
0.0992836079937302 |
| PARP1KO vs. ALC1KOPARP1KO in Olaparib 30nM |
-0.438331221724253 |
0.244066996853167 |
-1.79594630726725 |
0.110233745608729 |
| ALC1KOPARP1KO vs. PARP1KO in Olaparib 30nM |
0.438331221724252 |
0.244066996853167 |
1.79594630726725 |
0.110233745608729 |
| ALC1KOPARP1KO vs. ALC1KO in Olaparib 30nM |
2.2173492048566 |
0.244066996853167 |
9.08500220613841 |
1.72928582043794e-05 |
| ALC1KOPARP1KO vs. WT in Olaparib 30nM |
-0.0166654665999985 |
0.244066996853167 |
-0.0682823438435824 |
0.947236691399538 |
|
|
|
|
|
|
Estimate |
Std. Error |
t value |
Pr(>|t|) |
| WT vs. ALC1KO in Olaparib 300nM |
2.99862474465302 |
0.279359527256207 |
10.7339269009534 |
4.99039951809161e-06 |
| WT vs. PARP1KO in Olaparib 300nM |
-0.0225488104208594 |
0.279359527256207 |
-0.080716096001191 |
0.937650484949544 |
| WT vs. ALC1KOPARP1KO in Olaparib 300nM |
-0.130395816857611 |
0.279359527256207 |
-0.466767030064529 |
0.653114521477612 |
| ALC1KO vs. WT in Olaparib 300nM |
-2.99862474465302 |
0.279359527256207 |
-10.7339269009534 |
4.99039951809162e-06 |
| ALC1KO vs. PARP1KO in Olaparib 300nM |
-3.02117355507389 |
0.279359527256207 |
-10.8146429969546 |
4.71648981626386e-06 |
| ALC1KO vs. ALC1KOPARP1KO in Olaparib 300nM |
-3.12902056151064 |
0.279359527256207 |
-11.2006939310179 |
3.61876268369268e-06 |
| PARP1KO vs. ALC1KO in Olaparib 300nM |
3.02117355507388 |
0.279359527256207 |
10.8146429969546 |
4.71648981626387e-06 |
| PARP1KO vs. WT in Olaparib 300nM |
0.0225488104208593 |
0.279359527256207 |
0.0807160960011909 |
0.937650484949544 |
| PARP1KO vs. ALC1KOPARP1KO in Olaparib 300nM |
-0.107847006436751 |
0.279359527256207 |
-0.386050934063337 |
0.709523006620542 |
| ALC1KOPARP1KO vs. PARP1KO in Olaparib 300nM |
0.107847006436751 |
0.279359527256207 |
0.386050934063337 |
0.709523006620542 |
| ALC1KOPARP1KO vs. ALC1KO in Olaparib 300nM |
3.12902056151064 |
0.279359527256207 |
11.2006939310179 |
3.61876268369267e-06 |
| ALC1KOPARP1KO vs. WT in Olaparib 300nM |
0.130395816857611 |
0.279359527256207 |
0.466767030064529 |
0.653114521477611 |
|
|
|
|
|
|
Estimate |
Std. Error |
t value |
Pr(>|t|) |
| WT vs. ALC1KO in Olaparib 3000nM |
3.04754058178664 |
0.223223796352674 |
13.6524001095823 |
7.97659310937146e-07 |
| WT vs. PARP1KO in Olaparib 3000nM |
-0.491717229051973 |
0.223223796352674 |
-2.20279933002798 |
0.0587369379418308 |
| WT vs. ALC1KOPARP1KO in Olaparib 3000nM |
-0.301514965032195 |
0.223223796352674 |
-1.35072949192132 |
0.213742755890244 |
| ALC1KO vs. WT in Olaparib 3000nM |
-3.04754058178663 |
0.223223796352675 |
-13.6524001095823 |
7.9765931093716e-07 |
| ALC1KO vs. PARP1KO in Olaparib 3000nM |
-3.53925781083861 |
0.223223796352675 |
-15.8551994396103 |
2.50525637047875e-07 |
| ALC1KO vs. ALC1KOPARP1KO in Olaparib 3000nM |
-3.34905554681883 |
0.223223796352675 |
-15.0031296015036 |
3.84720496457958e-07 |
| PARP1KO vs. ALC1KO in Olaparib 3000nM |
3.53925781083861 |
0.223223796352674 |
15.8551994396103 |
2.50525637047871e-07 |
| PARP1KO vs. WT in Olaparib 3000nM |
0.491717229051973 |
0.223223796352674 |
2.20279933002798 |
0.0587369379418306 |
| PARP1KO vs. ALC1KOPARP1KO in Olaparib 3000nM |
0.190202264019778 |
0.223223796352674 |
0.852069838106663 |
0.418950633286543 |
| ALC1KOPARP1KO vs. PARP1KO in Olaparib 3000nM |
-0.190202264019778 |
0.223223796352674 |
-0.852069838106662 |
0.418950633286544 |
| ALC1KOPARP1KO vs. ALC1KO in Olaparib 3000nM |
3.34905554681883 |
0.223223796352674 |
15.0031296015037 |
3.84720496457951e-07 |
| ALC1KOPARP1KO vs. WT in Olaparib 3000nM |
0.301514965032195 |
0.223223796352674 |
1.35072949192132 |
0.213742755890244 |
|
|
|
|
|
|
Estimate |
Std. Error |
t value |
Pr(>|t|) |
| WT vs. ALC1KO in Olaparib 30000nM |
2.11535269897002 |
0.386296014823517 |
5.47598892506422 |
0.000590250711051459 |
| WT vs. PARP1KO in Olaparib 30000nM |
-0.708690088633984 |
0.386296014823517 |
-1.8345777886364 |
0.10390557266586 |
| WT vs. ALC1KOPARP1KO in Olaparib 30000nM |
-1.10612544718416 |
0.386296014823517 |
-2.86341407816363 |
0.0210407342023467 |
| ALC1KO vs. WT in Olaparib 30000nM |
-2.11535269897002 |
0.386296014823516 |
-5.47598892506421 |
0.000590250711051459 |
| ALC1KO vs. PARP1KO in Olaparib 30000nM |
-2.824042787604 |
0.386296014823516 |
-7.31056671370062 |
8.30354874963266e-05 |
| ALC1KO vs. ALC1KOPARP1KO in Olaparib 30000nM |
-3.22147814615418 |
0.386296014823517 |
-8.33940300322785 |
3.23405238481284e-05 |
| PARP1KO vs. ALC1KO in Olaparib 30000nM |
2.824042787604 |
0.386296014823517 |
7.31056671370062 |
8.30354874963269e-05 |
| PARP1KO vs. WT in Olaparib 30000nM |
0.708690088633983 |
0.386296014823517 |
1.8345777886364 |
0.10390557266586 |
| PARP1KO vs. ALC1KOPARP1KO in Olaparib 30000nM |
-0.397435358550181 |
0.386296014823517 |
-1.02883628952722 |
0.333655624920083 |
| ALC1KOPARP1KO vs. PARP1KO in Olaparib 30000nM |
0.39743535855018 |
0.386296014823517 |
1.02883628952722 |
0.333655624920084 |
| ALC1KOPARP1KO vs. ALC1KO in Olaparib 30000nM |
3.22147814615418 |
0.386296014823517 |
8.33940300322784 |
3.23405238481286e-05 |
| ALC1KOPARP1KO vs. WT in Olaparib 30000nM |
1.10612544718416 |
0.386296014823517 |
2.86341407816363 |
0.0210407342023467 |
write.table(output, file = "Figure2C_Stats_New_All.txt", quote = F, sep = "\t", row.names = T, col.names = NA)